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Relevant bibliographies by topics / Machine processing

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Academic literature on the topic 'Machine processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Machine processing"

1

S, Jones Arthi, Kousalya R, and Kumari M. "Manifest Electronic Voting Machine Using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1893–98. http://dx.doi.org/10.31142/ijtsrd11509.

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MURAMATSU, Hisayoshi, Takahiro ISHIKAWA, Takuya FUKUDA, Toshihiro FUJINOKI, Hideaki FUKUDA, and Gaku ASANUMA. "Electricity, Machines, Control, and Processing in Machine Tools." Journal of the Institute of Electrical Engineers of Japan 137, no. 7 (2017): 440–43. http://dx.doi.org/10.1541/ieejjournal.137.440.

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Righter, Rhonda. "Stochastic scheduling for a two-machine open shop." Journal of Applied Probability 34, no. 3 (September 1997): 733–44. http://dx.doi.org/10.2307/3215098.

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We study the problem of preemptive scheduling of jobs in a two-machine open shop. Jobs require processing on both machines, but the order does not matter. We define the D-LERPT (double longest expected remaining processing time) policy as the policy that first processes jobs that have not yet been processed by either machine (double jobs), in decreasing order of expected remaining processing times, and then processes jobs that require processing on only one machine in any order. We show that D-LERPT stochastically minimizes the makespan when preemption is not permitted and jobs (but not machines) are stochastically identical, and that D-LERPT minimizes the makespan in the increasing convex sense when preemption is permitted and the machines are stochastically identical and processing times are exponential or geometric with a job dependent rate.

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Righter, Rhonda. "Stochastic scheduling for a two-machine open shop." Journal of Applied Probability 34, no. 03 (September 1997): 733–44. http://dx.doi.org/10.1017/s002190020010138x.

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We study the problem of preemptive scheduling of jobs in a two-machine open shop. Jobs require processing on both machines, but the order does not matter. We define the D-LERPT (double longest expected remaining processing time) policy as the policy that first processes jobs that have not yet been processed by either machine (double jobs), in decreasing order of expected remaining processing times, and then processes jobs that require processing on only one machine in any order. We show that D-LERPT stochastically minimizes the makespan when preemption is not permitted and jobs (but not machines) are stochastically identical, and that D-LERPT minimizes the makespan in the increasing convex sense when preemption is permitted and the machines are stochastically identical and processing times are exponential or geometric with a job dependent rate.

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Zhi, Jing Zhang, Cheng Zu Ren, and Jing Chuan Dong. "Study on Post-Processing of Five-Axis Cnc Machining." Applied Mechanics and Materials 141 (November 2011): 524–28. http://dx.doi.org/10.4028/www.scientific.net/amm.141.524.

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According to the feature of five-axis CNC machine tools’structure, do researches on post-process technique. By means of analysis of the kinetic model of five-axis CNC machine tools, Position and orientation of the tool in the workpiece coordinate system transforms to that of the machine coordinate system. Besides, Because of the rotation，Five-axises maching will produce the non-linear error．This article provides an easy algorithm to find the maximum non-linear error. Finally, an impeller, for example, simulates in the computer and machine by a 5-axis machine tool.

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Sungheetha, Akey. "3D Image Processing using Machine Learning based Input Processing for Man-Machine Interaction." Journal of Innovative Image Processing 3, no. 1 (February 22, 2021): 1–6. http://dx.doi.org/10.36548/jiip.2021.1.001.

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In various real time applications, several assisted services are provided by the human-robot interaction (HRI). The concept of convergence of a three-dimensional (3D) image into a plane-based projection is used for object identification via digital visualization in robotic systems. Recognition errors occur as the projections in various planes are misidentified during the convergence process. These misidentifications in recognition of objects can be reduced by input processing scheme dependent on the projection technique. The conjoining indices are identified by projecting the input image in all possible dimensions and visualizing it. Machine learning algorithm is used for improving the processing speed and accuracy of recognition. Labeled analysis is used for segregation of the intersection without conjoined indices. Errors are prevented by identifying the non-correlating indices in the projections of possible dimension. The inputs are correlated with related inputs that are stored with labels thereby preventing matching of the indices and deviations in the planes. Error, complexity, time and recognition ratio metrics are verified for the proposed model.

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Залюбовський, М. Г., and І. В. Панасюк. "ЕКСПЕРИМЕНТАЛЬНЕ ДОСЛІДЖЕННЯ ВПЛИВУ ГЕОМЕТРІЇ МЕТАЛЕВИХ ВІДЛИВОК ДЕТАЛЕЙ ЗАМКА «БЛИСКАВКА» НА ОСОБЛИВОСТІ ЇХ ОБРОБКИ В ГАЛТУВАЛЬНІЙ МАШИНІ." Bulletin of the Kyiv National University of Technologies and Design. Technical Science Series 144, no. 2 (October 14, 2020): 24–32. http://dx.doi.org/10.30857/1813-6796.2020.2.2.

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Experimentally investigate the intensity and specificity of the processing of different types of metal castings of the zipper in a tensile machine with complex spatial movement of the working capacity, to obtain the appropriate calculation dependencies to determine the processing time. An experimental study of the separation from the casters of three types of metal castings of the zipper (casting with two, with eight fixed details-cases and outflow with sixteen fixed details by clamps) in a tensile machine with a complex spatial movement of the working capacity by observing the change in the number of machined parts during the process. Further mathematical processing of the obtained results is carried out. The types of castings for which the maximum and minimum technological time are spent in the toughening machine with complex spatial movement of the working capacity are determined. It is established that for processing castings with fixed parts-locks it takes twice more technological time, which is spent to clean the separated parts from the remnants of the wrap and burrs. Compared with conventional rotating shredding machines, using machines with complex spatial movement of the working capacity, you can increase the productivity of this technological operation by almost ten times. The dependences between different types of metallic castings of the zipper and the intensity of separation of parts from them during processing in a caulking machine with a complex spatial movement of the working capacity with providing a waterfall mode of movement and 50% filling the capacity. Appropriate design dependencies were obtained to determine the processing time. Guidelines on the implementation of rational technological parameters in the processing of various types of metal castings of the zipper in a tensile machine with a complex spatial movement of the working capacity. These guidelines can be used at the design stage of technological processes and equipment in the relevant machine-building and light industry enterprises specializing in the manufacture of hardware.

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TOYODA, Takatoshi. "Trends in Drawing Processing Machine." Journal of the Japan Society for Technology of Plasticity 52, no. 602 (2011): 336–40. http://dx.doi.org/10.9773/sosei.52.336.

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Rautiola, Kyösti, Tuomo Näyhä, and Kari Kaarela. "Parallel processing in machine automation." Microprocessing and Microprogramming 27, no. 1-5 (August 1989): 723–30. http://dx.doi.org/10.1016/0165-6074(89)90139-7.

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Jiang, Di. "Remodeling an RNA processing machine." Science 371, no. 6525 (January 7, 2021): 137.10–139. http://dx.doi.org/10.1126/science.371.6525.137-j.

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Dissertations / Theses on the topic "Machine processing"

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Morris, Todd D. (Todd Douglas) Carleton University Dissertation Engineering Electrical. ""VLSI triangulation processing for machine vision."." Ottawa, 1987.

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Bowman, C. C. "High speed image processing for machine vision." Thesis, Cardiff University, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383161.

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Park, Yongwon Baskiyar Sanjeev. "Dynamic task scheduling onto heterogeneous machines using Support Vector Machine." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Thesis/Park_Yong_50.pdf.

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Stymne, Sara. "Compound Processing for Phrase-Based Statistical Machine Translation." Licentiate thesis, Linköping : Department of Computer and Information Science, Linköpings universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51416.

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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.

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Alzubi, Omar A. "Designing machine learning ensembles : a game coalition approach." Thesis, Swansea University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678293.

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Grundström, Tobias. "Automated Measurements of Liver Fat Using Machine Learning." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151286.

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The purpose of the thesis was to investigate the possibility of using machine learn-ing for automation of liver fat measurements in fat-water magnetic resonancei maging (MRI). The thesis presents methods for texture based liver classificationand Proton Density Fat Fraction (PDFF) regression using multi-layer perceptrons utilizing 2D and 3D textural image features. The first proposed method was a data classification method with the goal to distinguish between suitable andunsuitable regions to measure PDFF in. The second proposed method was a combined classification and regression method where the classification distinguishes between liver and non-liver tissue. The goal of the regression model was to predict the difference d = pdff mean − pdff ROI between the manual ground truth mean and the fat fraction of the active Region of Interest (ROI).Tests were performed on varying sizes of Image Feature Regions (froi) and combinations of image features on both of the proposed methods. The tests showed that 3D measurements using image features from discrete wavelet transforms produced measurements similar to the manual fat measurements. The first method resulted in lower relative errors while the second method had a higher method agreement compared to manual measurements.

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Howlett, Robert J. "A distributed neural network for machine vision." Thesis, University of Brighton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260943.

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Muscedere, Roberto. "A multiple in-camera processing system for machine vision." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0023/MQ62258.pdf.

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Lai, Bing-Chang. "Combining generic programming with vector processing for machine vision." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060221.095043/index.html.

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Books on the topic "Machine processing"

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Klyepikov, Viktor. Processing equipment. Machine accessories. ru: INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/24563.

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Machine transcription for document processing. 3rd ed. Cincinnati, Ohio: South-Western Educational Pub., 1995.

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Swain, Debabala, Prasant Kumar Pattnaik, and Pradeep K. Gupta, eds. Machine Learning and Information Processing. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3.

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Agarwal, Sonali, Shekhar Verma, and Dharma P. Agrawal, eds. Machine Intelligence and Signal Processing. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4.

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Singh, Richa, Mayank Vatsa, Angshul Majumdar, and Ajay Kumar, eds. Machine Intelligence and Signal Processing. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2625-3.

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Swain, Debabala, Prasant Kumar Pattnaik, and Tushar Athawale, eds. Machine Learning and Information Processing. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4859-2.

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Vaclav, Hlavac, and Boyle Roger, eds. Image processing, analysis, and machine vision. 2nd ed. Pacific Grove, CA: PWS Pub., 1999.

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8

Sonka, Milan. Image processing, analysis, and machine vision. London: Chapman & Hall Computing, 1993.

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9

Batchelor, Bruce G. Interactive Image Processing for Machine Vision. London: Springer London, 1993.

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10

Mueller, Cheryl. Circles, Ltd.: Word processing machine transcription. Englewood Cliffs, NJ: Prentice-Hall, 1987.

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Book chapters on the topic "Machine processing"

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Beyerer, Jürgen, Fernando Puente León, and Christian Frese. "Morphological Image Processing." In Machine Vision, 607–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47794-6_12.

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Batchelor, Bruce G., and Frederick M. Waltz. "Morphological Image Processing." In Machine Vision Handbook, 801–70. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_19.

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Singh, Pramod. "Data Processing." In Machine Learning with PySpark, 23–42. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4131-8_3.

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Miller, John W. V., and Malayappan Shridhar. "Illumination-Invariant Image Processing." In Machine Vision Handbook, 543–64. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_13.

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Batchelor, Bruce G. "QT Image Processing Functions." In Machine Vision Handbook, 1561–959. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_41.

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Moyne, John A. "Lexical Processing." In Understanding Language: Man or Machine, 273–87. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2483-6_8.

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Moyne, John A. "Syntactic Processing." In Understanding Language: Man or Machine, 289–323. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2483-6_9.

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Camastra, Francesco, and Alessandro Vinciarelli. "Machine Learning." In Advanced Information and Knowledge Processing, 99–106. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8_4.

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Singh, Pramod. "Natural Language Processing." In Machine Learning with PySpark, 191–218. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4131-8_9.

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Tsujii, Junichi. "Machine translation." In Recent Advances in Natural Language Processing, 377. Amsterdam: John Benjamins Publishing Company, 1997. http://dx.doi.org/10.1075/cilt.136.32tsu.

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Conference papers on the topic "Machine processing"

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Otsubo, Tatsuki, Takanori Yazawa, Jinhui Wang, and Tomonori Kato. "Diamond Fly Cutting Applied to Improve Curved Surface Machining by In-Process Measurement and Control on an Ordinary Milling Machine." In JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/lemp2020-8590.

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Abstract To improve the accuracy of the machined surface produced by an ordinary milling machine, a system called workpiece-referred form accuracy control (WORFAC) was developed and confirmed in diamond turning. However, non-rotational symmetric surface structures, such as V-grooves, pyramid structures, F-theta lenses, and other free form surface cannot be machined by diamond turning. We proposed to improve the form accuracy of a machined surface produced by an ordinary milling machine by diamond fly cutting using controlled cutting with reference surface (CCRS), an in-process measurement and control method. Fly cutting is usually used to manufacture ultra-precision microstructures with nanometric surface roughness and submicrometric form accuracy, without the need for subsequent polishing. Nevertheless, a high level of accuracy has only recently been achieved on ultra-precision milling machines. In this study, we verified the effectiveness of fly cutting with CCRS on an ordinary milling machine. CCRS improves machined surface accuracy by controlling the relative displacement between the tool and workpiece. Diamond fly cutting using CCRS was demonstrated to reduce the table motion error on an ordinary milling machine. The experiments of curved surface machining by uncontrolled machining and control machining were conducted, and the effectiveness of improving the circular are machining accuracy of the general-purpose milling machine was confirmed.

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Stevens, J., A. Gee, and C. Dance. "Automatic Processing of Document Annotations." In British Machine Vision Conference 1998. British Machine Vision Association, 1998. http://dx.doi.org/10.5244/c.12.44.

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Baek, Ji Min, Kyeong Ha Lee, Seung Ho Lee, and Ja Choon Koo. "Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine." In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7445.

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Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.

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Tunley, H. "Distributed Dynamic Processing for Edge Detection." In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.3.

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Zwiggelaar, R. "Image Processing in Scale-Orientation Space." In British Machine Vision Conference 2002. British Machine Vision Association, 2002. http://dx.doi.org/10.5244/c.16.18.

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Shiegheun Koh, Youngjin Kim, and Woonseek Lee. "Scheduling two-machine flow shop with a batch processing machine." In Industrial Engineering (CIE39). IEEE, 2009. http://dx.doi.org/10.1109/iccie.2009.5223931.

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Baudino, Alberto, Giancarlo Colla, Giuseppe A. Marino, and Giancarlo Suci. "Processing sets on a SIMD machine." In the 1995 ACM symposium. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/315891.318010.

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Kokkinidis, K., A. Stergiaki, and A. Tsagaris. "Machine learning via multimodal signal processing." In 2017 6th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2017. http://dx.doi.org/10.1109/mocast.2017.7937653.

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Harvey, Dennis N. "Intelligent image processing for machine safety." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188935.

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Cyriac, Praveen, David Kane, and Marcelo Bertalmio. "Perceptual Dynamic Range for In-Camera Image Processing." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.19.

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Reports on the topic "Machine processing"

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K. LACKNER, D. BUTT, and C. WENDT. MATERIAL PROCESSING FOR SELF-ASSEMBLING MACHINE SYSTEMS. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/768220.

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Adams, T. A. Matched Field Processing on the Connection Machine. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada245469.

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Rosset, William S. de, and David Gray. Processing of Niobium-Lined M240 Machine Gun Barrels. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada612839.

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McClure, Donald E. Image Acquisition and Processing Equipment for Machine Vision. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada253902.

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Kohn, Adam, Elyse Sussman, and Odelia Schwartz. Linking Man and Machine Through Adaptive Sensory Processing. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada626013.

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Bhagavatula, Vijayakumar. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada535204.

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Homel, M. A., C. S. Sherman, and J. P. Morris. Machine Learning for Constitutive Modeling on a Graphics Processing Unit. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1576907.

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Milanfar, Peyman. Detection and Discrimination at the Intersection of Statistical Signal Processing and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada481960.

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Li, Eliot, Charles Nicholas, Tim Oates, and Raman K. Mehra. Intelligent Record Linkage Techniques Based on Information Retrieval, Natural Language Processing, and Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada408937.

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Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458739.

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